The Preference Learning Toolbox
This provides a practical resource for researchers and practitioners working with ordinal data, but it is incremental as it packages existing methods into a toolbox.
The authors tackled the need for accessible tools in preference learning by introducing an open-source, scalable toolbox that supports key phases of the data training process, including preprocessing, feature selection, and various learning methods.
Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the number of generated data of ordinal nature is increasing, the importance and role of the PL field becomes central within machine learning research and practice. This paper introduces an open source, scalable, efficient and accessible preference learning toolbox that supports the key phases of the data training process incorporating various popular data preprocessing, feature selection and preference learning methods.